Business processes may rely on computer tasks to aide in data processing, statistics, analytics, and/or the like. Tasks may be configured to run on a variety of data sources across multiple platforms. Typically, tools exist to assist in running various tasks. These tools may be dependent on static and defined task criteria such as system resource availability and utilization, time constraints, and/or the like. For example, a tool may be set to run a task at 5:00 p.m. every day, run every 10 minutes, run after a preceding task completes, run after 5 dependent tasks complete, run after a central processing unit (CPU) commit reaches 50%, and/or the like. In that regard, the tasks may run regardless of a need and/or requirement to run. The static and defined nature of the task criteria may cause inefficient resource utilization (e.g., with CPU, RAM, storage, and/or the like) and monetary waste due to tasks running more frequently than desired and/or needed. As such, there is an increased need for tools to provide an automated continuous task triggering based on predictive and dynamic dependencies.